This invention relates generally to ultrasound imaging systems and, more particularly, to adaptive optimization of ultrasound imaging system parameters using fuzzy logic controllers or neural network or both.
Diagnostic Ultrasound Imaging
Medical ultrasound imaging systems need to support a set of imaging modes for clinical diagnosis. The basic imaging modes are timeline Doppler, color flow velocity and power mode, B mode, and M mode. In B mode, the ultrasound imaging system creates two-dimensional images of tissue in which the brightness of a pixel is based on the intensity of the return echo. In color flow imaging mode, the movement of fluid (e.g., blood) or tissue can be imaged. Measurement of blood flow in the heart and vessels using the Doppler effect is well known. The phase shift of backscattered ultrasound waves can be used to measure the velocity of the moving tissue or blood. The Doppler shift may be displayed using different colors to represent speed and direction of flow. In the spectral Doppler imaging mode, the power spectrum of these Doppler frequency shifts is computed for visual display as velocity-time waveforms.
State-of-the-art ultrasound scanners may also support advanced or emerging imaging modes including contrast agent imaging, 3D imaging, spatial compounding, and extended field of view. Some of these advanced imaging modes involve additional processing of images acquired in one or more basic imaging modes, and attempt to provide enhanced visualization of the anatomy of interest.
A new trend in ultrasound technology development is the emergence of compact or portable ultrasound scanners that leverage the unceasing advances in system-on-a-chip technologies. It is anticipated that these compact scanners, though battery-operated, will support more and more of the imaging modes and functions of conventional cart-based scanners.
Regardless of physical size, ultrasound imaging systems are comprised of many subsystems. As shown in the ultrasound imaging system 10 of FIG. 1, the main signal path includes the transducer 12 with switch 13, transmitter 14, receiver 16, image processor(s) 18, display system 20, master controller 22, and user-input system 24. The transmitter 14, probe 12, and receiver subsystems 16 are responsible primarily for the acquisition, digitization, focusing and filtering of echo data. The image processing block 18 performs detection (e.g., echo amplitude for B-mode, mean velocity for color flow mode), and all subsequent pixel data manipulation (filtering, data compression, scan conversion and image enhancements) required for display. As used herein, the term pixel (derived from “picture element”) image data simply refers to detected image data, regardless of whether it has been scan converted into an x-y raster display format.
Conventional ultrasound systems generally require optimal adjustments of numerous system parameters involved in a wide range of system operations from data acquisition, image processing, and audio/video display. These system parameters can be divided into two broad categories: 1) user-selectable or adjustable; and 2) engineering presets. The former refers to all system parameters that the user can adjust via the user control subsystem. This includes imaging default parameters (e.g., gray map selection) that the user can program via the user control subsystem and store in system memory. In contrast, “engineering presets” refer a wide range of system processing and control parameters that may be used in any or all of the major subsystems for various system operations, and are generally pre-determined and stored in system memory by the manufacturer. These may include control or threshold parameters for specific system control mechanisms and/or data processing algorithms within various subsystems.
The need to optimize both kinds of system parameters is a long-standing challenge in diagnostic ultrasound imaging, mainly because (1) the majority of sonographers or users often lack the time and/or training to properly operate a very broad range of user-controls for optimal system performance; and (2) engineering presets are usually pre-determined by the manufacturer based on “typical” or “average” system operating conditions including patient type (body size and fat/muscle composition), normal and abnormal tissue characteristics for various application types, and environmental factors (e.g., ambient light condition).
For a compact scanner, user-control design is particularly challenging because the space available on the console for imaging control keys can be very limited. This means that the overall user-control subsystem will be restricted and/or more difficult to use (e.g., accessing multiple layers of soft-key menus) compared to conventional cart-based scanners.
Another related challenge for all ultrasound scanners is ergonomics. Even for an expert sonographer who is proficient at using all of the available system controls, the repetitive hand motions required to scan with a probe, and to adjust many control keys for each ultrasound examination protocol, are widely recognized as a source of repetitive stress injuries for sonographers.
There is need for more automated control of imaging parameters in ultrasound systems.
Fuzzy Logic Control
As taught by B. Kosko (Neural Networks and Fuzzy Systems, Prentice Hall, 1992), fuzzy logic is a methodology for estimating input-output functions that has proven very effective for “intelligent” control of a wide range of non-linear systems from subway system traffic to auto-focusing in camcorders. Fuzzy logic is especially suited to controlling systems whose inputs are multi-valued, or belong simultaneously to two or more contradictory sets of values (hence the name “fuzzy”). In fact, inputs in a fuzzy system are classified as members of different fuzzy sets with various degrees of membership. A key property of fuzzy inputs is that they exhibit continuous gradations between overlapping classes. As an example, precipitation can be classified as dense fog, drizzle, light rain, heavy rain, and downpour. Note that fuzzy terms are italicized throughout this disclosure.
Unlike statistical estimators, the fuzzy approach estimates a function without a mathematical model of how outputs depend on inputs. Instead, the mapping from inputs to outputs is defined by a set of linguistic rules or memory associations, similar to what an expert user would do. A fuzzy traffic controller might contain the fuzzy association “If traffic is heavy in this direction, then keep the light green longer.” One method of establishing the fuzzy rules is to pool the knowledge of many experts, and directly program them into the fuzzy controller. This is known as the Fuzzy Expert System approach.
For some applications, however, it is very difficult even for experts to articulate a set of rules that define the desired input-output behavior. For example, who can define a tree, a bomb, or his or her own face to the satisfaction of an automated detection system? Instead of using the fuzzy approach, such ill-defined problems may be better handled by a related but different methodology known as neural network. As taught by Kosko, neural network is a signal processing method that differs from fuzzy expert system in the way it associatively “inferences” or maps inputs to outputs. The neural approach requires the specification of a nonlinear dynamical system, the acquisition of a sufficiently representative set of numerical training samples, and the encoding of those training samples in the dynamical system by repeated learning cycles. In simple terms, the neural approach mimics how the neurons in the human brain conduct a learning process: it avoids direct definition but learns by pointing out examples.
Adaptive Ultrasound Imaging
Automatic system optimization techniques that can adapt to received ultrasound data and/or prevailing system operating conditions have received increasing attention in recent years. Referring to FIG. 1, these typically entail analyzing the pixel image data from the image processing unit 18, and adjusting different system parameters via, for example, the master controller 22. U.S. Pat. No. 6,398,733, for example, assumes that there exists a single optimal or correct answer and attempt to achieve that pre-determined optimal state based on mathematical or statistical methods, and binary logical decisions (e.g., “noise” or “tissue”) that utilize a set of fixed threshold values.
In reality, however, many of the inputs required for system or imaging control in an ultrasound system are not “black or white,” but are fuzzy: showing “many shades of gray” within overlapping classes. For example, the echo quality in a particular region of a gray-scale B-image can be classified as noisy echo, or strong echo. There is a need to develop more robust automatic system control methods that will work well with fuzzy input and/or output variables.
Although fuzzy logic and neural networks have been proposed for use in diagnostic ultrasound imaging systems, the existing technique is limited to computerized classification or segmentation of pixel image data within the image processing block 18 (see rectangle in dashed line of FIG. 1). Since the treatment of different pixels are often directly dependent on how they are classified, fuzzy logic provides a way of making decisions that can minimize false classifications. Examples include B-mode versus color flow data segmentation, and image registration for spatial compounding or 3D reconstruction.